Search (4 results, page 1 of 1)

0.09111415 = product of:
  0.13667122 = sum of:
    0.08786041 = weight(_text_:retrieval in 4115) [ClassicSimilarity], result of:
      0.08786041 = score(doc=4115,freq=4.0), product of:
        0.15490976 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.051211275 = queryNorm
        0.5671716 = fieldWeight in 4115, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.09375 = fieldNorm(doc=4115)
    0.04881081 = product of:
      0.09762162 = sum of:
        0.09762162 = weight(_text_:conference in 4115) [ClassicSimilarity], result of:
          0.09762162 = score(doc=4115,freq=2.0), product of:
            0.19418365 = queryWeight, product of:
              3.7918143 = idf(docFreq=2710, maxDocs=44218)
              0.051211275 = queryNorm
            0.50272834 = fieldWeight in 4115, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.7918143 = idf(docFreq=2710, maxDocs=44218)
              0.09375 = fieldNorm(doc=4115)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Source

SIGIR'04: Proceedings of the 27th Annual International ACM-SIGIR Conference an Research and Development in Information Retrieval. Ed.: K. Järvelin, u.a

0.0675026 = product of:
  0.1012539 = sum of:
    0.025886122 = weight(_text_:retrieval in 2648) [ClassicSimilarity], result of:
      0.025886122 = score(doc=2648,freq=2.0), product of:
        0.15490976 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.051211275 = queryNorm
        0.16710453 = fieldWeight in 2648, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2648)
    0.07536778 = sum of:
      0.040675674 = weight(_text_:conference in 2648) [ClassicSimilarity], result of:
        0.040675674 = score(doc=2648,freq=2.0), product of:
          0.19418365 = queryWeight, product of:
            3.7918143 = idf(docFreq=2710, maxDocs=44218)
            0.051211275 = queryNorm
          0.20947012 = fieldWeight in 2648, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.7918143 = idf(docFreq=2710, maxDocs=44218)
            0.0390625 = fieldNorm(doc=2648)
      0.034692105 = weight(_text_:22 in 2648) [ClassicSimilarity], result of:
        0.034692105 = score(doc=2648,freq=2.0), product of:
          0.17933317 = queryWeight, product of:
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.051211275 = queryNorm
          0.19345059 = fieldWeight in 2648, product of:
            1.4142135 = tf(freq=2.0), with freq of:
              2.0 = termFreq=2.0
            3.5018296 = idf(docFreq=3622, maxDocs=44218)
            0.0390625 = fieldNorm(doc=2648)
  0.6666667 = coord(2/3)

Abstract

The growing predominance of social semantics in the form of tagging presents the metadata community with both opportunities and challenges as for leveraging this new form of information content representation and for retrieval. One key challenge is the absence of contextual information associated with these tags. This paper presents an experiment working with Flickr tags as an example of utilizing social semantics sources for enriching subject metadata. The procedure included four steps: 1) Collecting a sample of Flickr tags, 2) Calculating cooccurrences between tags through mutual information, 3) Tracing contextual information of tag pairs via Google search results, 4) Applying natural language processing and machine learning techniques to extract semantic relations between tags. The experiment helped us to build a context sentence collection from the Google search results, which was then processed by natural language processing and machine learning algorithms. This new approach achieved a reasonably good rate of accuracy in assigning semantic relations to tag pairs. This paper also explores the implications of this approach for using social semantics to enrich subject metadata.

Source

Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas

0.03697917 = product of:
  0.055468753 = sum of:
    0.031063346 = weight(_text_:retrieval in 122) [ClassicSimilarity], result of:
      0.031063346 = score(doc=122,freq=2.0), product of:
        0.15490976 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.051211275 = queryNorm
        0.20052543 = fieldWeight in 122, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.046875 = fieldNorm(doc=122)
    0.024405405 = product of:
      0.04881081 = sum of:
        0.04881081 = weight(_text_:conference in 122) [ClassicSimilarity], result of:
          0.04881081 = score(doc=122,freq=2.0), product of:
            0.19418365 = queryWeight, product of:
              3.7918143 = idf(docFreq=2710, maxDocs=44218)
              0.051211275 = queryNorm
            0.25136417 = fieldWeight in 122, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.7918143 = idf(docFreq=2710, maxDocs=44218)
              0.046875 = fieldNorm(doc=122)
      0.5 = coord(1/2)
  0.6666667 = coord(2/3)

Abstract

This paper reports on a portion of a larger ongoing project. We address the issues of information organization and retrieval in large, active commercial websites. More specifically, we address the use of classification for providing access to the contents of such sites. We approach this analysis by describing the functionality and structure of the classification scheme of one such representative, large, active, commercial websites: eBay.com, a web-based auction site for millions of users and items. We compare eBay's classification scheme with the Art & Architecture Thesaurus, which is a tool for describing and providing access to material culture.

Source

Dynamism and stability in knowledge organization: Proceedings of the 6th International ISKO-Conference, 10-13 July 2000, Toronto, Canada. Ed.: C. Beghtol et al

0.017257415 = product of:
  0.051772244 = sum of:
    0.051772244 = weight(_text_:retrieval in 4277) [ClassicSimilarity], result of:
      0.051772244 = score(doc=4277,freq=8.0), product of:
        0.15490976 = queryWeight, product of:
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.051211275 = queryNorm
        0.33420905 = fieldWeight in 4277, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          3.024915 = idf(docFreq=5836, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4277)
  0.33333334 = coord(1/3)

Abstract

This chapter reviews research and applications in statistical language modeling for information retrieval (IR), which has emerged within the past several years as a new probabilistic framework for describing information retrieval processes. Generally speaking, statistical language modeling, or more simply language modeling (LM), involves estimating a probability distribution that captures statistical regularities of natural language use. Applied to information retrieval, language modeling refers to the problem of estimating the likelihood that a query and a document could have been generated by the same language model, given the language model of the document either with or without a language model of the query. The roots of statistical language modeling date to the beginning of the twentieth century when Markov tried to model letter sequences in works of Russian literature (Manning & Schütze, 1999). Zipf (1929, 1932, 1949, 1965) studied the statistical properties of text and discovered that the frequency of works decays as a Power function of each works rank. However, it was Shannon's (1951) work that inspired later research in this area. In 1951, eager to explore the applications of his newly founded information theory to human language, Shannon used a prediction game involving n-grams to investigate the information content of English text. He evaluated n-gram models' performance by comparing their crossentropy an texts with the true entropy estimated using predictions made by human subjects. For many years, statistical language models have been used primarily for automatic speech recognition. Since 1980, when the first significant language model was proposed (Rosenfeld, 2000), statistical language modeling has become a fundamental component of speech recognition, machine translation, and spelling correction.